This method is illustrated in Figure REF , it is based on MBISPLD [1]}, a model-based interpretable approach. The only remarkable differences are (i) a modified inverse tone mapping block and (ii) LocalNet, a custom CNN added on top to improve performance in exchange of losing invertibility and efficiency.
The “inverse tone mapping" is formulated as as a convex combination of several monotonic curves, parameterized in a polynomial form. Note that these curves are fixed and not learned together with the model.
The “LocalNet" is designed to predict conditions for feature modulation, and can be viewed as a variant of local mapping. The output maintains fine details and inverts only tone and color. Authors emphasize in their ablation studies that this LocalNet contributes significantly to improve performance. The team adopts Half Instance Normalization (HIN) [2]} as a basic component in this network.
Ablation studies: The original MBISPLD [1]} achieved 34.02dB using 0.54M parameters (8 GMACs) and a runtime of 14ms, after adding the custom inverse tone mapping and LocalNet, the model can achieve 36.20dB (improvement of 2dB) in exchange of being \(\times 10\)  more complex (80 GMACs, 5.7M parameters) and \(\times 2\)  slower.
Authors also compare with other methods such as UPI [4]}, DRN [5]} and ESRGAN [6]}; the base method MBISPLD [1]} and their proposed modification achieved the best results.
